Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation

Haq, Rabia (Memorial Sloan-Kettering Cancer Center, Sloan Kettering Institute, Department of Medical Physics) ; Schmid, Jerome (Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland) ; Borgie, Roderick (Naval Medical Center, San Diego) ; Cates, Joshua (OrthoGrid Systems, Utah) ; Audette, Michel A. (Old Dominion University, Virginia)

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation.


Mots-clés:
Type d'article:
scientifique
Faculté:
Santé
Ecole:
HEdS - Genève
Institut:
Aucun institut
Date:
2020-01
Pagination:
38 p.
Veröffentlicht in:
Journal of medical imaging
Numérotation (vol. no.):
2020, vol. 7, no. 1, article 015002
DOI:
ISSN:
2329-4302
Le document apparaît dans:



 Datensatz erzeugt am 2020-06-08, letzte Änderung am 2021-03-01

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